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10 Newer Entries Showing entries 61 to 70 of 217 10 Older Entries

Displaying posts with tag: mongodb (reset)

MongoDB, TokuMX and InnoDB for disk IO-bound, update-only by PK
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I used sysbench to measure TPS for a workload that does 1 update by primary key per transaction. The database was much larger than RAM and the server has a SAS disk array that can do at least 2000 IOPs with a lot of concurrency. The update is to a non-indexed column so there is no secondary index maintenance which also means there is no benefit from a fractal tree in TokuMX or the change buffer in InnoDB. I also modified the benchmark …

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How TokuMX Secondaries Work in Replication
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As I’ve mentioned in previous posts, TokuMX replication differs quite a bit from MongoDB’s replication. The differences are large enough such that we’ve completely redone some of MongoDB’s existing algorithms. One such area is how secondaries apply oplog data from a primary. In this post, I’ll explain how.

In designing how secondaries …

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Why aren't you using X, version 2
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Sometimes I get asked why am I not using product X where X is anything but MySQL. The products that are suggested change over time and the value of X very much depends on the person asking the question. An ex-manager from my days at Oracle told me that Oracle would be better and developers from the SQL Server team told me the same. For those keeping score there was a social network that ran SQL Server and they were kind of enough to explain why.



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MongoDB, TokuMX and InnoDB for concurrent inserts
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I used the insert benchmark with concurrent insert threads to understand performance limits in MongoDB, TokuMX and InnoDB. The database started empty and eventually was much larger than RAM. The benchmark requires many random writes for secondary index maintenance for an update-in-place b-tree used by MongoDB and InnoDB. The test server has fast flash storage. The work per transaction for this test is inserting 1000 documents/rows where each document/row is small (100 bytes) and has 3 secondary indexes to maintain. The test used 10 client connections to run these transactions concurrently and each client uses a separate collection/table. The performance …

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MongoDB, TokuMX and InnoDB for disk IO-bound, read-only point queries
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This repeats a test that was done on pure-flash servers. The goals are to determine whether the DBMS can efficiently use the IO capacity of a pure-disk server.  The primary metrics are the QPS that the DBMS can sustain and the ratio of disk reads per query. The summary is that a clustered primary key index makes TokuMX and InnoDB much more IO efficient for PK lookups on IO-bound workloads.

TokuMX and InnoDB get much more QPS than MongoDB from the same IO capacity for this workload. TokuMX and InnoDB have a clustered …

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Notes on the storage stack
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If you want high performance and quality of service from a DBMS then you need the same from the OS. The MySQL/Postgres/MongoDB crowd doesn't always speak with the Linux crowd. On the bright side there is a good collection of experts from the Linux side of things at my employer and we have begun speaking. There were several long threads on the PG hackers lists about PG+Linux and this lead to a meeting at the LSFMM summit. I am very happy these groups met. We have a lot to learn from each other. DBMS people can explain our IO …

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Insert benchmark on disks, part 2
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I ran more insert benchmark tests for InnoDB on pure disk servers. The previous results with a lot more detail are here. My goal in this case was to use better configuration options for InnoDB on disk and to understand the impact of innodb_flush_neighbors. With the better settings InnoDB sustains a much higher insert rate.

The first problem in the previous tests was that I used a few settings that are better …

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Insert benchmark for flash, part 2
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I repeated a few of the long running tests for the insert benchmark using flash storage. My goals were to test at least one new configuration, repeat a few tests to confirm the configuration was what I claimed it was and to confirm the impact of doing fsync-on-commit during this test. In this test the write operation adds 1000 small documents and the redo log write is not small. From casual observation I did not see a big impact from doing fsync-on-commit (or a big benefit from not doing it) but that is the point of this post. From the …

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TokuMX, MongoDB and InnoDB on IO-bound point queries
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I used sysbench to understand whether TokuMX, MongoDB and InnoDB can use most of the IOPs provided by a fast flash device and whether their use is efficient. The workload query fetches one document/row by primary key. This is a very simple workload but helps me to understand how disk read requests are processed. The primary metric is the QPS that can be sustained when the database is much larger than RAM. A secondary metric is the number of disk reads per query during the test. Efficiency, not doing too many reads per query, matters when you want to support many concurrent users.

tl;dr - new database engines are usually worse on …

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on io scheduling again
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Most of database engines have to deal with underlying layers – operating systems, device drivers, firmware and physical devices, albeit different camps choose different methods.
In MySQL world people believe that InnoDB should be handling all the memory management and physical storage operations – maximized buffer pool space, adaptive/fuzzy flushing, crash recovery getting faster, etc. That can result in lots of efficiency wins, as managing everything with data problem in mind allows to tune for efficiency and performance.

Other storage systems (though I hear it from engineers on different types of problems too) like PostgreSQL or MongoDB consider OS to be much smarter and …


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10 Newer Entries Showing entries 61 to 70 of 217 10 Older Entries

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